Pothole detection using location-aware convolutional neural networks

Poor road conditions, such as potholes, are a nuisance to society, which would annoy passengers, damage vehicles, and even cause accidents. Thus, detecting potholes is an important step toward pavement maintenance and rehabilitation to improve road conditions. Potholes have different shapes, scales, shadows, and illumination effects, and highly complicated backgrounds can be involved. Therefore, detection of potholes in road images is still a challenging task. In this study, we focus on pothole detection in 2D vision and present a new method to detect potholes based on location-aware convolutional neural networks, which focuses on the discriminative regions in the road instead of the global context. It consists of two main subnetworks: the first localization subnetwork employs a high recall network model to find as many candidate regions as possible, and the second part-based subnetwork performs classification on the candidates on which the network is expected to focus. The experiments using the public pothole dataset show that the proposed method could achieve high precision (95.2%), recall (92.0%) simultaneously, and outperform the most existing methods. The results also demonstrate that accurate part localization considerably increases classification performance while maintains high computational efficiency. The source code is available at .